Anomaly detection inspired by immune network theory: A proposal

Hui Keng Lau; Jon Timmis; Iain Bate

doi:10.1109/CEC.2009.4983328

Anomaly detection inspired by immune network theory: A proposal

Hui Keng Lau, Jon Timmis, Iain Bate

Department of Computer Science

University of York

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

4 Citations (Scopus)

Abstract

Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.

Original language	English
Title of host publication	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Publisher	IEEE Press
Pages	3045-3051
Number of pages	7
ISBN (Print)	9781424429592
DOIs	https://doi.org/10.1109/CEC.2009.4983328
Publication status	Published - 2009
Event	2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway Duration: 18 May 2009 → 21 May 2009

Publication series

Name	2009 IEEE Congress on Evolutionary Computation, CEC 2009

Conference

Conference	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Country/Territory	Norway
City	Trondheim
Period	18 May 2009 → 21 May 2009

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10.1109/CEC.2009.4983328

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title = "Anomaly detection inspired by immune network theory: A proposal",

abstract = "Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.",

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language = "English",

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series = "2009 IEEE Congress on Evolutionary Computation, CEC 2009",

publisher = "IEEE Press",

pages = "3045--3051",

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address = "United States of America",

note = "2009 IEEE Congress on Evolutionary Computation, CEC 2009 ; Conference date: 18-05-2009 Through 21-05-2009",

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Lau, HK, Timmis, J & Bate, I 2009, Anomaly detection inspired by immune network theory: A proposal. in 2009 IEEE Congress on Evolutionary Computation, CEC 2009., 4983328, 2009 IEEE Congress on Evolutionary Computation, CEC 2009, IEEE Press, pp. 3045-3051, 2009 IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18 May 2009. https://doi.org/10.1109/CEC.2009.4983328

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N2 - Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.

AB - Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.

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ER -

Anomaly detection inspired by immune network theory: A proposal

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